Senior Data Scientist

Posted 7 days agoViewed

💎 Seniority level: Senior, 3+ years

📍 Location: United States

💸 Salary: 170000.0 - 200000.0 USD per year

🔍 Industry: Healthcare

🏢 Company: SmarterDx👥 101-250💰 $50,000,000 Series B 11 months agoArtificial Intelligence (AI)Hospital Information Technology Health Care

🗣️ Languages: English

⏳ Experience: 3+ years

🪄 Skills: AWSPythonSQLCloud ComputingData AnalysisMachine LearningPyTorchSnowflakeData scienceData modeling

Requirements:

3+ years of experience in data science, machine learning, or a related field, preferably in a product-driven environment.
Strong proficiency in Python and experience with machine learning frameworks such as PyTorch, or scikit-learn.
Deep understanding of statistical modeling, optimization techniques, and data analysis.
Experience working with structured and text data, including feature engineering and data preprocessing.
Ability to translate business objectives into data science problems and effectively communicate results to stakeholders.
Experience deploying machine learning models into production and optimizing model performance based on real-world feedback.
Strong collaboration skills and the ability to work across cross-functional teams including Engineering, Product, and Analytics.
Familiarity with cloud platforms (AWS, GCP, or Azure) and MLOps best practices.

Responsibilities:

Develop and refine modeling approaches in close collaboration with the Machine Learning Research team, iterating on experiments to improve model performance.
Execute rapid experimentation cycles, documenting learnings and identifying promising avenues for further development.
Support deployment efforts by creating standardized model endpoints and interfaces for seamless integration with product workflows.
Contribute to shared modeling infrastructure, building tools and utilities that accelerate experimentation and standardize workflows across teams.
Collaborate with Engineering, Analytics, and Product teams to integrate machine learning models into product workflows, ensuring they drive measurable business KPIs.
Adapt and enhance existing modeling approaches to drive impact in new product areas.
Identify product needs and communicate them effectively across the Data Science and Machine Learning Research Science teams.

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